Zenith has unveiled 10 trends that show how artificial intelligence will help to power the consumer journey in 2017, driving engagement opportunities and effectiveness for marketers.
Artificial intelligence is the ability of machines or computers to emulate human thinking or decision making. A key area within AI is machine learning and Zenith’s 2017 Trends build on the network’s ground breaking automation of digital planning, unveiled earlier this year. Using bespoke algorithms, a team of data scientists and strategists from Zenith developed sophisticated machine learning technology that enabled the network to create an ‘automation loop’: data collection, attribution and planning changes across multiple touchpoints – all done automatically. Our 10 trends assess how machine learning and other areas of AI will enhance the consumer experience along the journey to purchase and will create new marketing opportunities for brands.
Predicting Our Needs: AI enhances the Role of Search in the Consumer Journey
Search is becoming increasingly predictive, providing tailored recommendations throughout the consumer journey to drive both consideration and conversation. During 2017, search engines will begin to factor in additional behavioural data – artificial intelligence technology will use this information to power predictive search. Enhanced predictive search gives clear opportunities for brands to better anticipate consumer needs in order to serve more relevant ads and also to cross-sell products.
Speed is the New Black: Turbo-charging the Delivery of Trend Content
In recent years, there has been an explosion in the quantity of consumer data available to marketers. This enables brands to quickly spot trends and react to this in their marketing to consumers. With data set to grow in quantity, machine learning will significantly help to streamline the process, digesting data from a variety of sources to quickly identify underlying patterns. Using AI in trend analysis will help marketers to stay ahead of both the trend curve and the competition. Content specialists will be able to create a pool of assets that can be quickly served to consumers in line with trend analysis, and product development teams will be better equipped to stay on top of the latest category demands.
Always-on Insights: Non-stop data collection through the Passive User Interface
The Passive User Interface continually collects behavioural data from consumers’ digital devices and by applying machine learning techniques can provide brands with powerful insights than can be used to customise consumer experiences. Companies are already using PUI data, for example Spotify’s running platform uses data from fitness trackers to customise its playlists for consumers. Greater use of PUI data will enable brands to design personalised content and services and to set appropriate pricing strategies. PUI data can also be shared by brands across different categories to help improve multiple consumer experience points.
Cross-Device Storytelling: Advances in Programmatic Automate Brand Conversations
Machine learning technology is starting to help brands to tie their conversations to specific individuals. Brands have plenty of first party data, but this specific application of AI links individuals to their devices and helps brands to understand how consumer engagements and brand actions can be attributed to different messages in different contexts at different times. Brands can then automate their conversations with consumers using cross-device programmatic advertising. This will really help to create seamless experiences, and to accelerate both purchase and re-purchase.
Shoppable Content: Buying Direct from Branded Content Enhances Consumer Experience
2017 will be the year of ‘shoppable content’: purchasing items directly from editorial and branded content. ‘Evolutionary algorithms’ can tweak and optimise content in response to consumer’s navigation, creating live content. Universal shopping carts recreate the functionality of e-commerce sites without consumers having to create new accounts or provide credit card details for each new site they visit. This combination of technologies will enable brands and publishers to keep consumers on their sites rather than forcing them to go elsewhere to buy. Brands will need to treat content as a compelling combination of text, images and interactive features that create a shopping experience.
Smart VR: Brand Opportunities as Virtual Reality Moves to Smartphones
Virtual reality is moving from the solitary world of gamers to the mainstream of consumers experiencing VR through their smartphones. Facebook and Twitter already have live streams that can be accessed using headsets attached to smartphones. The shift to smartphones and to mainstream applications will present brands with many marketing opportunities. For example, retailers with the opportunity to transform how people shop – trying out products without having to visit a store.
The Rise of the Chatbot: All hail frictionless communication between brands and consumers
Powered by machine learning, chatbots enable automated interaction between consumers and brands via a messaging interface. While there are obvious limitations with automated communication, chatbots can help consumers with process functions such as making payments and notifying of delivery/shipping. Chatbots can help brands to reduce customer support costs and to open up greater dialogue with consumers. There is also a great opportunity for brands to create personalised recommendations for consumers based on insights from the trails of chats.
Playing to Our Emotions: Emotion Recognition Technology Helps Brands to Tap into Human Truths
The spread of smartphones and the rise of embedded emotion recognition technology means that many people now carry mood-sensing devices in their pockets. This gives brands the opportunity to match consumers’ moods and behaviours with relevant content at the right moment. For example, brands that have an association with a particular sport or team could use this technology to offer more relevant experiences based on consumer reactions during a sporting event.
Dynamic Pricing: Algorithms enable automated demand-led pricing
Driven by high-performance computing and analytics, Dynamic Pricing enables retailers to price items at a point determined by a particular customer’s perceived ability and willingness to pay. Pricing on some website and apps now changes from minute to minute. For example, Uber introduced its Surge Pricing algorithm to enable pricing to automatically rise at times of peak demand.
Automated Assistance: Service Robots Hit the High Street Stores
Industrial robots have been in use for many years. Now, technology is blending physical and digital automation to create service robots, working alongside humans. The most obvious and immediate opportunities are in retail and hospitality. Service robots will be able to provide pricing and stock availability information and using algorithms will be able offer discounts and related product suggestions. The opportunities could stretch beyond retail and hospitality into healthcare and domestic help.